# RAGScore

> Use this tool when you need to generate high-quality QA datasets and evaluate the performance of Retrieval-Augmented Generation (RAG) systems, with support for any large language model (LLM) and deployment options for local or cloud environments. It solves problems related to data preparation and model evaluation for RAG systems, providing a privacy-first approach. The tool takes in LLM and dataset configurations as inputs and outputs evaluated RAG system performance metrics.

Canonical page: https://skillsregistry.net/skills/io-github-hzyai-ragscore  
JSON: https://api.skillsregistry.net/v1/skills/io-github-hzyai-ragscore

## Description

Generate QA datasets & evaluate RAG systems with failure diagnosis. Any LLM.

## Trust

- **Trust score (0–1):** 0.94
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 0.6.10
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/io.github.HZYAI%2Fragscore)
- **Repository:** <https://github.com/HZYAI/RagScore>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "io-github-hzyai-ragscore"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/io-github-hzyai-ragscore` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/io-github-hzyai-ragscore/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt